The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation because photoreceptor adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing, suggesting rapid post receptor luminance gain control. Similarly, in the fruit fly Drosophila, such gain control leads to luminance invariant behavior for moving OFF stimuli. Here we show that behavioral responses to moving ON stimuli also utilize a luminance gain, and that ON-motion guided behavior depends on inputs from three first-order interneurons L1, L2 and L3. Each of these neurons encodes contrast and luminance differently and distributes information asymmetrically across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2 and L3 are not specific inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavior in varying conditions.

Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation, because adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing. The fruit fly Drosophila also shows nearly luminance invariant behavior for both ON and OFF stimuli. To achieve this, first-order interneurons L1, L2 and L3 all encode contrast and luminance differently, and distribute information across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2 and L3 are not distinct inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavioral performance in varying conditions.

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